The Clinical Value of the ECG in Noncardiac Conditions

Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium

Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability. Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability. Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells. Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.

Best practice considerations for nonclinical in vivo cardiovascular telemetry studies in non-rodent species: Delivering high quality QTc data to support ICH E14/S7B Q&As

The ICH E14/S7B Questions and Answers (Q&As) guideline introduces the concept of a "double negative" nonclinical scenario (negative hERG assay and negative in vivo QTc study) to demonstrate that a drug does not produce a clinically relevant QT prolongation (i.e., no QT liability). This nonclinical "double negative" data package, along with negative Phase 1 clinical QTc data, may be sufficient to substitute for a clinical Thorough QT (TQT) study in some specific cases. While standalone GLP in vivo cardiovascular studies in non-rodent species are standard practice during nonclinical drug development for small molecule programs, a variety of approaches to the design, conduct, analysis and interpretation are utilized across pharmaceutical companies and contract research organizations (CROs) that may, in some cases, negatively impact the stringent sensitivity needed to fulfill the new Q&As. Subject matter experts from both Pharma and CROs have collaborated to recommend best practices for more robust nonclinical cardiovascular telemetry studies in non-rodent species, with input from clinical and regulatory experts. The aim was to increase consistency and harmonization across the industry and to ensure delivery of high quality nonclinical QTc data to meet the proposed sensitivities defined within the revised ICH E14/S7B Q&As guideline (Q&As 5.1 and 6.1). The detailed best practice recommendations presented here cover the design and execution of the safety pharmacology cardiovascular study, including optimal methods for acquiring, analyzing, reporting, and interpreting the resulting QTc and pharmacokinetic data to allow for direct comparison to clinical exposures and assessment of safety margin for QTc prolongation.

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient's information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network

Objective.Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias.Approach.In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length.Main results.The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database.Significance.MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.

Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet

Electrocardiogram (ECG) is mostly used for the clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the deep neural networks have been applied to the automatic classification of cardiac arrhythmia with great success. However, most models independently extract the internal features of each lead in the 12-lead ECG during the training phase, resulting in a lack of inter-lead features. Here, we propose a general model based on the two-dimensional ECG and ResNet with detached squeeze-and-excitation modules (DSE-ResNet) to realize the automatic classification of normal rhythm and 8 cardiac arrhythmias. The original 12-lead ECG is spliced into a two-dimensional plane like a grayscale picture. DSE-ResNet is used to simultaneously extract the internal and inter-lead features of the two-dimensional ECG. Furthermore, an orthogonal experiment method is used to optimize the hyper-parameters of DSE-ResNet and a multi-model voting strategy is used to improve classification performance. Experimental results based on the test set of China Physiological Signal Challenge 2018 (CPSC2018) show that our model has average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1= 0.817$$\end{document}F1=0.817 for classifying normal rhythm and 8 cardiac arrhythmias. Meanwhile, compared with the state-of-art model in CPSC2018, our model achieved the best \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1 in 2 sub-abnormal types. This shows that the model based on the two-dimensional ECG and DSE-ResNet has advantage in detecting some cardiac arrhythmias and has the potential to be used as an auxiliary tool to help doctors perform cardiac arrhythmias analysis.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis

Objective: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We aimed to develop new markers based on the morphology of the QRS complex of the electrocardiogram (ECG). Methods: ECG recordings from 29 patients undergoing hemodialysis (HD) were processed. Mean warped QRS complexes were computed in two-minute windows at the start of an HD session, at the end of each HD hour and 48 h after it. We quantified QRS width, amplitude and the proposed QRS morphology-based markers that were computed by warping techniques. Reference [K+] and [Ca2+] were determined from blood samples acquired at the time points where the markers were estimated. Linear regression models were used to estimate electrolyte levels from the QRS markers individually and in combination with T wave morphology markers. Leave-one-out cross-validation was used to assess the performance of the estimators. Results: All markers, except for QRS width, strongly correlated with [K+] (median Pearson correlation coefficients, r, ranging from 0.81 to 0.87) and with [Ca2+] (r ranging from 0.61 to 0.76). QRS morphology markers showed very low sensitivity to heart rate (HR). Actual and estimated serum electrolyte levels differed, on average, by less than 0.035 mM (relative error of 0.018) for [K+] and 0.010 mM (relative error of 0.004) for [Ca2+] when patient-specific multivariable estimators combining QRS and T wave markers were used. Conclusion: QRS morphological markers allow non-invasive estimation of [K+] and [Ca2+] with low sensitivity to HR. The estimation performance is improved when multivariable models, including T wave markers, are considered. Significance: Markers based on the QRS complex of the ECG could contribute to non-invasive monitoring of serum electrolyte levels and arrhythmia risk prediction in patients with renal disease.

Estimation of potassium levels in hemodialysis patients by T wave nonlinear dynamics and morphology markers

OBJECTIVE

Noninvasive screening of hypo- and hyperkalemia can prevent fatal arrhythmia in end-stage renal disease (ESRD) patients, but current methods for monitoring of serum potassium (K+) have important limitations. We investigated changes in nonlinear dynamics and morphology of the T wave in the electrocardiogram (ECG) of ESRD patients during hemodialysis (HD), assessing their relationship with K+ and designing a K+ estimator.

METHODS

ECG recordings from twenty-nine ESRD patients undergoing HD were processed. T waves in 2-min windows were extracted at each hour during an HD session as well as at 48 h after HD start. T wave nonlinear dynamics were characterized by two indices related to the maximum Lyapunov exponent (λt, λwt) and a divergence-related index (η). Morphological variability in the T wave was evaluated by three time warping-based indices (dw, reflecting morphological variability in the time domain, and da and daNL, in the amplitude domain). K+was measured from blood samples extracted during and after HD. Stage-specific and patient-specific K+ estimators were built based on the quantified indices and leave-one-out cross-validation was performed separately for each of the estimators.

RESULTS

The analyzed indices showed high inter-individual variability in their relationship with K+. Nevertheless, all of them had higher values at the HD start and 48 h after it, corresponding to the highest K+. The indices η and dw were the most strongly correlated with K+ (median Pearson correlation coefficient of 0.78 and 0.83, respectively) and were used in univariable and multivariable linear K+ estimators. Agreement between actual and estimated K+ was confirmed, with averaged errors over patients and time points being 0.000 ± 0.875 mM and 0.046 ± 0.690 mM for stage-specific and patient-specific multivariable K+ estimators, respectively.

CONCLUSION

ECG descriptors of T wave nonlinear dynamics and morphological variability allow noninvasive monitoring of K+ in ESRD patients.

SIGNIFICANCE

ECG markers have the potential to be used for hypo- and hyperkalemia screening in ESRD patients.

Synopsis of Biomarkers of Atheromatous Plaque Formation, Rupture and Thrombosis in the Diagnosis of Acute Coronary Syndromes

Acute coronary syndrome is the main cause of mortality and morbidity worldwide and early diagnosis is a challenge for clinicians. Though cardiac Troponin, the most commonly used biomarker, is the gold standard for myocardial necrosis, it is blind for ischemia without necrosis. Therefore, ideal biomarkers are essential in the care of patients presenting with symptoms suggestive of cardiac ischemia. The ideal biomarker or group of biomarkers of atheromatous plaque formation, rupture and thrombosis for timely and accurate diagnosis of acute coronary syndrome is a current need. Therefore, we discuss the existing understanding and future of biomarkers of atheromatous plaque formation, rupture and thrombosis of acute coronary syndrome in this review. Keywords were searched from Medline, ISI, IBSS and Google Scholar databases. Further, the authors conducted a manual search of other relevant journals and reference lists of primary articles. The development of high-sensitivity troponin assays facilitates earlier exclusion of acute coronary syndrome, contributing to a reduced length of stay at the emergency department, and earlier treatment resulting in better outcomes. Although researchers have investigated biomarkers of atheromatous plaque formation, rupture and thrombosis to help early diagnosis of cardiac ischemia, most of them necessitate validation from further analysis. Among these biomarkers, pregnancy-associated plasma protein-A, intercellular adhesion molecule-1, and endothelial cell-specific molecule- 1(endocan) have shown promising results in the early diagnosis of acute coronary syndrome but need further evaluation. However, the use of a combination of biomarkers representing varying pathophysiological mechanisms of cardiac ischemia will support risk assessment, diagnosis and prognosis in these patients and this is the way forward.

Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([K+]) variations (Δ[K+]) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [K+] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects Δ[K+] and two time-warping-based T-wave morphological parameters, dw and its heart-rate corrected version dw,c, have been shown to reliably track Δ[K+] from the ECG. The aim of this study is to derive polynomial models relating dw and dw,c with Δ[K+], and to test their ability to reliably sense and quantify Δ[K+] values. Methods: 48-hour Holter ECGs and [K+] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, dw and dw,c were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s (ρ) and Pearson’s (r) correlation coefficients, and the estimation error (ed) between Δ[K+] and the corresponding model-estimated values (Δ^[K+]) were calculated. Results and Discussions: Nonlinear models were the most suitable for Δ[K+] estimation, rendering higher Pearson’s correlation (median 0.77 ≤r≤ 0.92) and smaller estimation error (median 0.20 ≤ed≤ 0.43) than the linear model (median 0.76 ≤r≤ 0.86 and 0.30 ≤ed≤ 0.40), even if similar Spearman’s ρ were found across models (median 0.77 ≤ρ≤ 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as Δ[K+] sensors in ESRD-HD patients.

Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

OBJECTIVE

Chronic kidney disease affects more than 10% of the world population. Changes in serum ion concentrations increase the risk for ventricular arrhythmias and sudden cardiac death, particularly in end-stage renal disease (ESRD) patients. We characterized how T wave amplitude, duration and morphology descriptors change with variations in serum levels of potassium and calcium and in heart rate, both in ESRD patients and in simulated ventricular fibers.

METHODS

Electrocardiogram (ECG) recordings from twenty ESRD patients undergoing hemodialysis (HD) and pseudo-ECGs (pECGs) calculated from twenty-two simulated ventricular fibers at varying transmural heterogeneity levels were processed to quantify T wave width ( T_w), T wave slope-to-amplitude ratio ([Formula: see text]) and four indices of T wave morphological variability based on time warping ( d_w, [Formula: see text], d_a and [Formula: see text]). Serum potassium and calcium levels and heart rate were measured along HD.

RESULTS

[Formula: see text] was the marker most strongly correlated with serum potassium, dw with calcium and da with heart rate, after correction for covariates. Median values of partial correlation coefficients were 0.75, -0.74 and -0.90, respectively. For all analyzed T wave descriptors, high inter-patient variability was observed in the pattern of such relationships. This variability, accentuated during the first HD time points, was reproduced in the simulations and shown to be influenced by differences in transmural heterogeneity.

CONCLUSION

Changes in serum potassium and calcium levels and in heart rate strongly affect T wave descriptors, particularly those quantifying morphological variability.

SIGNIFICANCE

ECG markers have the potential to be used for monitoring serum ion concentrations in ESRD patients.

Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?

Diseases caused by alterations of ionic concentrations are frequently observed challenges and play an important role in clinical practice. The clinically established method for the diagnosis of electrolyte concentration imbalance is blood tests. A rapid and non-invasive point-of-care method is yet needed. The electrocardiogram (ECG) could meet this need and becomes an established diagnostic tool allowing home monitoring of the electrolyte concentration also by wearable devices. In this review, we present the current state of potassium and calcium concentration monitoring using the ECG and summarize results from previous work. Selected clinical studies are presented, supporting or questioning the use of the ECG for the monitoring of electrolyte concentration imbalances. Differences in the findings from automatic monitoring studies are discussed, and current studies utilizing machine learning are presented demonstrating the potential of the deep learning approach. Furthermore, we demonstrate the potential of computational modeling approaches to gain insight into the mechanisms of relevant clinical findings and as a tool to obtain synthetic data for methodical improvements in monitoring approaches.

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

Background

The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results.

Objective

Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model.

Methods

Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K⁺) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K⁺ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians.

Results

In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively.

Conclusions

A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

Objectives

Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert.

Methods

To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset.

Results

When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96).

Conclusions

The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.

Electrocardiographic artifact potentially misleading to the wrong management

Atrial flutter (AF) is the second most common supraventricular tachyarrhythmia following atrial fibrillation. We present an interesting case of a diagnostic challenge manifested as an electrocardiogram (ECG) recording mimicking AF in a patient with Parkinson's disease (PD). A 72-year-old African-American female with history of PD presented to our Emergency Department with a one day history of chest pain. Her vital signs were within normal limits. Physical exam was remarkable for bilateral resting hand tremors at a frequency of 6-8 hertz and mild cogwheel rigidity in both upper extremities. Initial ECG was interpreted as AF prompting admission. After careful review of her ECG by a cardiologist, several features such as, sharply contoured upright p waves in all leads, different flutter wave morphologies in the same leads, more prominence of "pseudo-flutter" waves in the limb leads compared to the precordial leads, and return to isoelectric baseline after sharp peaked p waves, questioned the diagnosis of AF. A repeat 12 lead ECG clearly demonstrated normal sinus rhythm, and the patient remained completely asymptomatic throughout the stay. A 48-hour Holter monitoring in the clinic later confirmed consistent sinus rhythm with no evidence of any arrhythmias Tremor induced artifacts can be mistaken for arrhythmias. Correct and accurate diagnosis is critically important, in order to avoid wrong treatment and unnecessary interventions. Our case illustrates the importance of recognizing artifact related ECG changes to prevent unnecessary treatment and hospital admissions.

A window-based time series feature extraction method

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets.

A Wearable Hydration Sensor with Conformal Nanowire Electrodes

A wearable skin hydration sensor in the form of a capacitor is demonstrated based on skin impedance measurement. The capacitor consists of two interdigitated or parallel electrodes that are made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. The flexible and stretchable nature of the AgNW/PDMS electrode allows conformal contact to the skin. The hydration sensor is insensitive to the external humidity change and is calibrated against a commercial skin hydration system on an artificial skin over a wide hydration range. The hydration sensor is packaged into a flexible wristband, together with a network analyzer chip, a button cell battery, and an ultralow power microprocessor with Bluetooth. In addition, a chest patch consisting of a strain sensor, three electrocardiography electrodes, and a skin hydration sensor is developed for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless, and continuous monitoring of skin hydration and other health parameters.

Hypothermia and targeted temperature management in cats and dogs

OBJECTIVE

To review current knowledge surrounding the effects, treatment, and prognosis of hypothermia in people, dogs, and cats, as well as the application of therapeutic hypothermia in clinical medicine.

ETIOLOGY

Hypothermia may be a primary or secondary condition, and may be due to environmental exposure, illness, medications, anesthesia, or trauma. Hypothermia has been applied therapeutically in human medicine for a variety of conditions, including postcardiac arrest. In veterinary medicine, the technique has been applied in cardiac surgeries requiring bypass and in a patient with intractable seizures.

DIAGNOSIS

Hypothermia can be diagnosed based on presenting temperature or clinical signs, and appropriate diagnosis may require nontraditional thermometers.

THERAPY

Rewarming is the primary treatment for accidental hypothermia, with intensity ranging from passive surface rewarming to extracorporeal rewarming. The goal is to return the core temperature to a level that restores normal physiologic function of all body processes. Other supportive therapies such as intravenous fluids are typically indicated, and if cardiopulmonary arrest is present, prolonged resuscitation may be required. In cases of secondary hypothermia, reversal of the underlying cause is important.

PROGNOSIS

There are few prognostic indicators in human and veterinary patients with hypothermia. Even the most severely affected individuals, including those presenting in cardiopulmonary arrest, have potential for complete recovery with appropriate therapy. Therapeutic hypothermia has been shown to improve outcome in people following cardiac arrest. Further studies are needed to examine this application in veterinary medicine, as well as appropriate therapy and prognosis for cases of spontaneous hypothermia.

The Normal Electrocardiogram: Resting 12-Lead and Electrocardiogram Monitoring in the Hospital

The electrocardiogram (ECG) is a well-established diagnostic tool extensively used in clinical settings. Knowledge of cardiac rhythm and mastery of cardiac waveform interpretation are fundamental for intensive care nurses. Recognition of the normal findings for the 12-lead ECG and understanding the significance of changes from baseline in continuous cardiac monitoring are essential steps toward ensuring safe patient care. This article highlights historical developments in electrocardiography, describes the normal resting 12-lead ECG, and discusses the need for continuous cardiac monitoring. In addition, future directions for the ECG are explored briefly.

Analysis of myocardial infarction signals using optical technique

The risk of heart attack or myocardial infarction (MI) may lead to serious consequences in mortality and morbidity. Current MI management in the triage includes non-invasive heart monitoring using an electrocardiogram (ECG) and the cardic biomarker test. This study is designed to explore the potential of photoplethysmography (PPG) as a simple non-invasive device as an alternative method to screen the MI subjects. This study emphasises the usage of second derivative photoplethysmography (SDPPG) intervals as the extracted features to classify the MI subjects. The statistical analysis shows the potential of "a-c" interval and the corrected "a-cC" interval to classify the subject. The sensitivity of the predicted model using "a-c" and "a-cC" is 90.6% and 81.2% and the specificity is 87.5% and 84.4%, respectively.

Out in the cold: the hypothermic heart response

We present an interesting case of a 49-year-old woman with hypothermia and associated Osborn waves (also called J waves) on ECG. She was found on the floor of her home and difficult to arouse. On arrival to the emergency department (ED), her rectal temperature was 87.5°F. ECG showed Osborn waves in diffuse leads. She was intubated in the ED and was started on vasopressor support for hypotension refractory to intravenous fluid boluses. She was transferred to the critical care unit for continued respiratory and cardiovascular support. With active external rewarming her core body temperature continued to improve. Blood pressure also improved and vasopressor was tapered off. She was extubated and was transferred to the medical floor for continued supportive care. Osborn waves on ECG resolved within 12 h of achieving normal range body temperature. The patient was eventually discharged home with medical follow-up.

Electrocardiogram artifact caused by rigors mimicking narrow complex tachycardia: a case report

BACKGROUND

The electrocardiogram (ECG) is useful in the diagnosis of cardiac and non-cardiac conditions. Rigors due to shivering can cause electrocardiogram artifacts mimicking various cardiac rhythm abnormalities.

CASE PRESENTATION

We describe an 80-year-old Sri Lankan man with an abnormal electrocardiogram mimicking narrow complex tachycardia during the immediate post-operative period. Electrocardiogram changes caused by muscle tremor during rigors could mimic a narrow complex tachycardia.

CONCLUSIONS

Identification of muscle tremor as a cause of electrocardiogram artifact can avoid unnecessary pharmacological and non-pharmacological intervention to prevent arrhythmias.

Clinical Significance of Conditions Presenting with ECG Changes Mimicking Acute Myocardial Infarction

The electrocardiogram (ECG) is the primary tool in the diagnosis of acute myocardial infarction (AMI). However, other clinical conditions, both cardiac and noncardiac originated pathologies, may result in ECG tracing of AMI. This may lead to an incorrect diagnosis, exposing the patients to unnecessary tests and potentially harmful therapeutic procedures. The aim of this report is to increase the still insufficient awareness of clinicians from multiple disciplines, regarding the different clinical syndromes, both cardiac and noncardiac, associated with ECG abnormalities mimicking AMI, to avoid unjustified thrombolytic therapy or intervention procedures. During a 9-year period, the data from six patients (five females, one male; mean age, 50 years [range, 18 to 78 years]) who were admitted to cardiac care unit (CCU) with transient ECG changes resembling AMI were recorded retrospectively. During this 9-year period, 5,400 patients were hospitalized in CCU: 1,350 patients were diagnosed as ST-elevation myocardial infarction (STEMI) and 4,050 patients were diagnosed as non-ST-elevation myocardial infarction (NSTEMI). Only two out of six patients had chest pain with ECG changes criteria suspicious of AMI. STEMI was suspected in four out of six patients. All patients, but one, had normal left ventricular (LV) function. One patient had transient LV dysfunction. All patients, but one, with perimyocarditis, had normal serum cardiac markers. In four out of six patients, who underwent coronary arteries imaging during hospitalization (by angiography or by CT scan), normal coronary arteries were documented. Two patients who underwent ambulatory cardiac CT scan imaging after being discharged from hospital documented patent coronary arteries (case no. 3), or some insignificant irregularities (case no. 4). The discharge diagnoses from CCU were as follows: postictal syndrome, pericarditis, hypothermia, stress-induced ("tako-tsubo") cardiomyopathy, anaphylactic reaction, and status of postchemotherapy. All patients experienced full recovery with normal ECG tracing. During the 5-year follow-up, all patients were alive, and cardiac morbidity was not reported. We conclude that both cardiac and noncardiac clinical syndromes may mimic AMI. Comprehensive clinical examination and profound medical history are crucial for making the correct diagnosis in conditions with ECG changes mimicking AMI.

A fateful kiss: the use of CT coronary angiogram in the diagnosis of non-cardiac chest pathology

A 34-year-old dental nurse presented with a 2-day history of retrosternal chest pain that was constant and 'burning' in nature. She was otherwise fit and well, no significant prior medical history and no ischaemic heart disease risk factors. Clinical examination was entirely normal apart from pyrexia of 38.3°C. Admission ECG showed ST depression in the inferior and anterolateral leads suggestive of myocardial ischaemia, consequently a CT coronary angiogram (CTCA) was performed. This showed normal coronary arteries, incidental distal oesophageal thickening was seen. Further history taking revealed that her youngest daughter had recently suffered from cold sores. The patient went on to have a diagnostic procedure, an oesophagogastroduodenoscopy. Biopsies confirmed acute oesophagitis with features suggestive of herpes virus infection. The patient responded promptly to oral acyclovir. This case highlights the value of CT coronary angiogram in identifying non-cardiac pathology in patients with a low pretest probability of coronary artery disease.

Difficulties in diagnosing pulmonary embolism in the obese patient: a literature review

Several of the signs and symptoms of pulmonary embolism, such as dyspnea, tachypnea, and tachycardia, are common in the obese population, so these patients are frequently suspected of having a pulmonary embolism. Establishing an accurate diagnosis in this situation is often difficult. We performed a review of the literature examining the difficulty of diagnosing pulmonary embolism in obese patients. Several factors compromise the ability of clinicians to accurately diagnose pulmonary embolism in obese patients. When patients weigh over 350 lbs (159 kg), thoracic imaging often cannot be performed because of the weight limitations of the scanning equipment. If equipment is available that can scan these patients, image quality is often poor. The literature that is available to guide the clinician in this difficult clinical scenario is sparse. Access to radiology equipment that can image morbidly obese patients is improving, but questions about image quality remain. Further research on both imaging and outcomes is needed.

Successful therapeutic hypothermia in patients with congenital long QT syndrome

Therapeutic hypothermia has been shown to improve neurological outcomes in patients who remain comatose following resuscitation from cardiac arrest. While there are numerous reports of patients who have had a successful course after induction of therapeutic hypothermia, such therapeutic intervention has not been described in patients with congenital long QT syndrome (LQTS). We report outcomes in two patients with LQTS who had therapeutic hypothermia following a ventricular fibrillation arrest. Careful and routine monitoring of the QT interval in this patient population is necessary due to the potential for worsening electrical instability during induced hypothermia.

J deflections on ECG in severe hypothermia and hypokalaemia: a case report

The J wave, also known as Osborn wave, is a deflection that can be observed on the surface ECG as a late delta wave, seen at the end of the QRS complex. In this case, a 75-year-old woman, after 1 day of continuous haemodialysis, showed a marked hypothermia (28.5°C) and severe hypokalaemia (1.7 mEq/l). Bradycardia was seen on the monitor and J waves were recognised on the ECG recording. After appropriate replacement of potassium and treatment of hypothermia, the J waves disappeared spontaneously.

12-lead ECG findings of pulmonary hypertension occur more frequently in emergency department patients with pulmonary embolism than in patients without pulmonary embolism

STUDY OBJECTIVE

Acute pulmonary embolism can produce abnormalities on ECG that reflect severity of pulmonary hypertension. Early recognition of these findings may alter the estimated pretest probability of pulmonary embolism and prompt more aggressive treatment before hemodynamic instability ensues, but it is first important to test whether these findings are specific to patients with pulmonary embolism. We hypothesize that ECG findings consistent with pulmonary hypertension would be observed more frequently in patients with pulmonary embolism.

METHODS

Secondary analysis of a prospective, observational cohort of emergency department patients who were tested for pulmonary embolism. ECGs were ordered at clinician's discretion and interpreted at presentation.

RESULTS

Six thousand forty-nine patients had an ECG, 354 (5.9%) of whom were diagnosed with pulmonary embolism. The frequency, positive likelihood ratio (LR+) and 95% confidence interval (CI) of each predictor were as follows: S1Q3T3 8.5% with pulmonary embolism versus 3.3% without pulmonary embolism (LR+ 3.7; 95% CI 2.5 to 5.4); nonsinus rhythm, 23.5% versus 16.6% (LR+ 1.4; 95% CI 1.2 to 1.7); inverted T waves in V1 to V2, 14.4% versus 8.1% (LR+ 1.8; 95% CI 1.3 to 2.3); inversion in V1 to V3, 10.5% versus 4.0% (LR+ 2.6; 95% CI 1.9 to 3.6); inversion in V1 to V4, 7.3% versus 2.0% (LR+ 3.7; 95% CI 2.4 to 5.5); incomplete right bundle branch block, 4.8% versus 2.8% (LR+ 1.7; 95% CI 1.0 to 2.7); tachycardia (pulse rate >100 beats/min), 28.8% versus 15.7% (LR+ 1.8; 95% CI 1.5 to 2.2). Likelihood ratios and specificities were similar when patients with previous cardiopulmonary disease were excluded from analysis.

CONCLUSION

Findings of acute pulmonary hypertension were infrequent overall but were observed more frequently in patients with the final diagnosis of pulmonary embolism compared with patients who do not have pulmonary embolism.

[Perioperative myocardial damage in non-cardiac surgery patients]

Perioperative myocardial damage occurs with a high incidence depending on the operative procedure and the patients examined and is considered to be among the most relevant risk factors for increased perioperative morbidity and mortality in patients undergoing non-cardiac surgery. The pathophysiology of myocardial damage in the perioperative period is still not well understood. Both ischemia with and without acute coronary occlusion and non-ischemic stimuli can put a substantial strain on the heart in the perioperative period. However, in many cases the clinical presentation does not allow a clear differentiation between ischemic and non-ischemic myocardial damage. In the majority of cases perioperative myocardial infarctions occur with only mild or even without any clinical symptoms. This is probably due to a considerable difference in phenotype and pathophysiology between perioperative and non-perioperative myocardial infarctions. As a result of this unexplained etiology of perioperative myocardial infarction it remains an open question whether the contemporary diagnostic and therapeutic recommendations for the acute coronary syndrome can be extrapolated to the perioperative situation. The present review reflects the current state of knowledge and presents an optional approach to the diagnosis and therapy of perioperative myocardial injury.

Cardiac risk after craniopharyngioma therapy

Although long-term survival after craniopharyngioma treatment is excellent in childhood and early adulthood, sudden deaths in two craniopharyngioma survivors with cardiac findings suggest a need to determine whether treated patients exhibit potential substrates for sudden cardiac death. We present a retrospective review of two index patients with cardiac mortality. This motivated a prospective cardiac screening of 12 survivors that identified nearly a third with significant QTc prolongation. QTc-prolonging medication and stimulants should be used with caution in this population, and routine electrocardiogram screening may identify those at highest risk.